Partitionning medical image databases for content-based queries on a grid

articleInternational audienceIn this article we study the impact of executing a medical image database query application on the grid. For lowering the total computation time, the image database is partitioned in subsets to be processed on different grid nodes. A theoretical model of the application computation cost and estimates of the grid execution overhead are used to efficiently partition the database. We show results demonstrating that smart partitioning of the database can lead to significant improvements in terms of total computation time

Bridging clinical information systems and grid middleware: a Medical Data Manager

International audienceThis paper describes the effort to deploy a Medical Data Management service on top of the EGEE grid infrastructure. The most widely accepted medical image stan- dard, DICOM, was developed for fulfilling clinical practice. It is implemented in most medical image acquisition and analysis devices. The EGEE middleware is us- ing the SRM standard for handling grid files. Our prototype is exposing an SRM compliant interface to the grid middleware, transforming on the fly SRM requests into DICOM transactions. The prototype ensures user identification, strict file ac- cess control and data protection through the use of relevant grid services. This Medical Data Manager is easing the access to medical databases needed for many medical data analysis applications deployed today. It offers a high level data man- agement service, compatible with clinical practices, which encourages the migration of medical applications towards grid infrastructures. A limited scale testbed has been deployed as a proof of concept of this new service. The service is expected to be put into production with the next EGEE middleware generation

Grid technology for biomedical applications

International audienceThe deployment of biomedical applications in a grid environment has started about three years ago in several European projects and national ini-tiatives. These applications have demonstrated that the grid paradigm was rele-vant to the needs of the biomedical community. They have also highlighted that this community had very specific requirements on middleware and needed fur-ther structuring in large collaborations in order to participate to the deployment of grid infrastructures in the coming years. In this paper, we propose several ar-eas where grid technology can today improve research and healthcare. A cru-cial issue is to maximize the cross fertilization among projects in the perspec-tive of an environment where data of medical interest can be stored and made easily available to the different actors of healthcare, the physicians, the health-care centres and administrations, and of course the citizens

Authentication and autorisation prototype on the microgrid for medical data management

International audienceThis paper presents μgrid, a light weight middleware for grid applications, and focuses mainly on security issues -more specifically on the access control to resources - that are critical for the gridification of many medical applications. For this purpose, we use Sygn as a distributed, certificate based, and flexible access control mechanism, which has been fully integrated in μgrid. We discuss the advantages of the solution compared to classical grid approaches and the limitations of the final architecture

A formal architecture-centric and model driven approach for the engineering of science gateways

From n-Tier client/server applications, to more complex academic Grids, or even the most recent and promising industrial Clouds, the last decade has witnessed significant developments in distributed computing. In spite of this conceptual heterogeneity, Service-Oriented Architecture (SOA) seems to have emerged as the common and underlying abstraction paradigm, even though different standards and technologies are applied across application domains. Suitable access to data and algorithms resident in SOAs via so-called ‘Science Gateways’ has thus become a pressing need in order to realize the benefits of distributed computing infrastructures.In an attempt to inform service-oriented systems design and developments in Grid-based biomedical research infrastructures, the applicant has consolidated work from three complementary experiences in European projects, which have developed and deployed large-scale production quality infrastructures and more recently Science Gateways to support research in breast cancer, pediatric diseases and neurodegenerative pathologies respectively. In analyzing the requirements from these biomedical applications the applicant was able to elaborate on commonly faced issues in Grid development and deployment, while proposing an adapted and extensible engineering framework. Grids implement a number of protocols, applications, standards and attempt to virtualize and harmonize accesses to them. Most Grid implementations therefore are instantiated as superposed software layers, often resulting in a low quality of services and quality of applications, thus making design and development increasingly complex, and rendering classical software engineering approaches unsuitable for Grid developments.The applicant proposes the application of a formal Model-Driven Engineering (MDE) approach to service-oriented developments, making it possible to define Grid-based architectures and Science Gateways that satisfy quality of service requirements, execution platform and distribution criteria at design time. An novel investigation is thus presented on the applicability of the resulting grid MDE (gMDE) to specific examples and conclusions are drawn on the benefits of this approach and its possible application to other areas, in particular that of Distributed Computing Infrastructures (DCI) interoperability, Science Gateways and Cloud architectures developments

Using Grid Technologies to Face Medical Image Analysis Challenges

The availability of digital imagers inside hospitals and their ever growing inspection capabilities have established digital medical images as a key component of many pathologies diagnosis, follow-up and treatment. To face the growing image analysis requirements, automated medical image processing algorithms have been developed over the two past decades. In parallel, medical image databases have been set up in health centers. Some attempts have been made to cross data coming from different origins for studies involving large databases. Grid technologies appear to be a promising tool to face the raising challenges of computational medicine. They offer wide area access to distributed databases in a secured environment and they bring the computational power needed to complete some large scale statistical studies involving image processing. In this paper, we review grid-related requirements of medical application that we illustrate through two real examples